Osteoporotic hip fracture prediction from risk factors available in administrative claims data - A machine learning approach

TY - JOUR

T1 - Osteoporotic hip fracture prediction from risk factors available in administrative claims data - A machine learning approach

AU - Engels, Alexander

AU - Reber, Katrin C

AU - Lindlbauer, Ivonne

AU - Rapp, Kilian

AU - Büchele, Gisela

AU - Klenk, Jochen

AU - Meid, Andreas

AU - Becker, Clemens

AU - König, Hans-Helmut

PY - 2020

Y1 - 2020

N2 - OBJECTIVE: Hip fractures are among the most frequently occurring fragility fractures in older adults, associated with a loss of quality of life, high mortality, and high use of healthcare resources. The aim was to apply the superlearner method to predict osteoporotic hip fractures using administrative claims data and to compare its performance to established methods.METHODS: We devided claims data of 288,086 individuals aged 65 years and older without care level into a training (80%) and a validation set (20%). Subsequently, we trained a superlearner algorithm that considered both regression and machine learning algorithms (e.g., support vector machines, RUSBoost) on a large set of clinical risk factors. Mean squared error and measures of discrimination and calibration were employed to assess prediction performance.RESULTS: All algorithms used in the analysis showed similar performance with an AUC ranging from 0.66 to 0.72 in the training and 0.65 to 0.70 in the validation set. Superlearner showed good discrimination in the training set but poorer discrimination and calibration in the validation set.CONCLUSIONS: The superlearner achieved similar predictive performance compared to the individual algorithms included. Nevertheless, in the presence of non-linearity and complex interactions, this method might be a flexible alternative to be considered for risk prediction in large datasets.

AB - OBJECTIVE: Hip fractures are among the most frequently occurring fragility fractures in older adults, associated with a loss of quality of life, high mortality, and high use of healthcare resources. The aim was to apply the superlearner method to predict osteoporotic hip fractures using administrative claims data and to compare its performance to established methods.METHODS: We devided claims data of 288,086 individuals aged 65 years and older without care level into a training (80%) and a validation set (20%). Subsequently, we trained a superlearner algorithm that considered both regression and machine learning algorithms (e.g., support vector machines, RUSBoost) on a large set of clinical risk factors. Mean squared error and measures of discrimination and calibration were employed to assess prediction performance.RESULTS: All algorithms used in the analysis showed similar performance with an AUC ranging from 0.66 to 0.72 in the training and 0.65 to 0.70 in the validation set. Superlearner showed good discrimination in the training set but poorer discrimination and calibration in the validation set.CONCLUSIONS: The superlearner achieved similar predictive performance compared to the individual algorithms included. Nevertheless, in the presence of non-linearity and complex interactions, this method might be a flexible alternative to be considered for risk prediction in large datasets.

U2 - 10.1371/journal.pone.0232969

DO - 10.1371/journal.pone.0232969

M3 - SCORING: Journal article

C2 - 32428007

VL - 15

SP - e0232969

JO - PLOS ONE

JF - PLOS ONE

SN - 1932-6203

IS - 5

ER -